Scientists and Swindlers: Consulting on Coal and Oil in America, 1820–1890
Bibliographic record
Abstract
In this impressively researched and highly original work, Paul Lucier explains how science became an integral part of American technology and industry in the nineteenth century. Scientists and Swindlers introduces us to a new service of professionals: the consulting scientists. Lucier follows these entrepreneurial men of science on their wide-ranging commercial engagements from the shores of Nova Scotia to the coast of California and shows how their innovative work fueled the rapid growth of the American coal and oil industries and the rise of American geology and chemistry. Along the way, he explores the decisive battles over expertise and authority, the high-stakes court cases over patenting research, the intriguing and often humorous exploits of swindlers, and the profound ethical challenges of doing science for money. Starting with the small surveying businesses of the 1830s and reaching to the origins of applied science in the 1880s, Lucier recounts the complex and curious relations that evolved as geologists, chemists, capitalists, and politicians worked to establish scientific research as a legitimate, regularly compensated, and respected enterprise. This sweeping narrative enriches our understanding of how the rocks beneath our feet became invaluable resources for science, technology, and industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".